Simplex GA and hybrid methods
Grégory Seront, Hugues Bersini · 2002
These last years two global optimizations methods hybridizing Evolutionary Algorithms (EA, but mainly GA) with hill-climbing methods have been investigated. The first one involves two interwoven levels of optimization: Evolution (EA) and Individual Learning (hill-climbing), which cooperate in the global optimization process. The second one consists of modifying EA by the introduction of new genetic operators or by the alteration of traditional ones in such a way that these new operators reflect basic mechanisms of hill-climbing methods. Since we believe these two methods of hybridization to be complementary rather than redundant (the first method makes the hill-climbing perform locally whereas the second globally), a complete hybridization is advocated.